A Review on Artificial Intelligence & Machine Learning Are Used for Drug Discovery in Prostate Cancer

Abstract

Artificial intelligence (AI) and Machine learning are transforming drug discovery in prostate cancer by addressing the biological heterogeneity, therapeutic resistance, and inefficiencies inherent in traditional pipelines. By integrating multi-omics, imaging, and clinical datasets, Artificial intelligence/Machine learning enables target identification, drug–target interaction prediction, lead optimization, and biomarker discovery with improved accuracy and speed. Advances in deep learning, graph machine learning, and explainable AI have enhanced structural prediction, virtual screening, and patient stratification, facilitating precision oncology approaches. Emerging platforms, start-ups, and industry–academic collaborations are further accelerating the translation of computational models into clinically relevant therapeutics, particularly for castration-resistant prostate cancer Despite challenges, including data heterogeneity, interpretability, and regulatory complexities, Artificial intelligence driven approaches offer unparalleled opportunities for personalized treatment, clinical trial optimization, and drug repurposing. This review highlights the conceptual frameworks, translational applications, industry landscape, and future directions of Artificial intelligence /Machine Learning in prostate cancer drug discovery, underscoring their potential to deliver efficient, equitable, and patient-specific therapeutics.

Int. J. Appl. Sci. Biotechnol. Vol 14(3): 110-121.